Plant-Inspired AI: Plants as Inspiration for Novel Problem Formulations, and Two Case Studies
Authors: Deepayan Sanyal, Joel Michelson, Carla E. Cao, Adam B. Roddy, Maithilee Kunda
Organizations: Department of Environmental Studies, New York University, New York City, NY, 10003, USA · Department of Philosophy, Universidad de Murcia, Murcia, 30010, Spain · School of Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AB, UK
Artificial Intelligence (AI) has long been inspired by studies of biological intelligence. Reinforcement learning, for instance, drew inspiration from studies involving animal learning and is now a powerful paradigm for solving many real-world problems. Recently, plant biologists have uncovered a wide range of complex behaviors in plants that enable them to flexibly adapt to variable environments. Here, we argue that such behavior can motivate new AI frameworks encompassing a range of problems overlooked by existing problem-solving frameworks such as supervised learning, tree search, and constraint satisfaction. We illustrate this idea with two examples of intelligent problem-solving in plants: (1) leaf mimicry in Boquila trifoliolata, a vine capable of altering its leaves' morphology to resemble those of multiple host trees simultaneously; and (2) coordinated root-shoot growth, wherein plants allocate resources across organ systems exploring distinct environments. While leaf mimicry is highly specific to Boquila, coordination of root-shoot growth is shared across most plants. For both examples, we capture underlying computational principles and identify problems fitting these frameworks that are currently unaddressed by AI. Finally, we outline preliminary task formulations and discuss how these formulations may be applied to non-plant problems.
Figures & tables
Figure 1: Overview of the two case studies. (a) Examples of leaf mimicry in B. trifoliolata ( Gianoli and Carrasco-Urra, 2014 ) . Host tree leaves are marked with T , while the Boquila leaves are marked with V . (b) The ratio of auxin to cytokinin determines the extent to which roots and shoots grow in the plant ( Kurepa et al., 2023 ) . Different environmental conditions result in different optimal ratios.
Figure 2: Instantiation of the collective mimicry task described in Section 3.3 . The left image is a target to be imitated, sampled from the MNIST dataset ( LeCun et al., 1998 ) . The second image shows a cell graph G that might have been grown in an attempt to solve this task instance. This graph is not shaped like the digit depicted in T , so it is not a particularly good solution in this case. Edges between cells indicate the neighbors whose hidden states each cell may observe, while dotted arrows show where new cells may be grown. The central image shows the receptive fields of the cells; these fields depend on each cell’s position relative to contiguous cells in its row and column. The fourth image shows the mean value of each field; these are the cells’ observations of the target. Ultimately, the goal of the cells is to grow into the shape of the target image; the rightmost image shows the error in the appearance of the grown cell structure compared with the target T .
Figure 3: Overview of the root-shoot collaboration task. (a) The environment is divided into aboveground and belowground regions with different nutrient distributions. A shoot cell S can acquire Carbon and Sunlight, while a root cell R can acquire Nitrogen and Water. (b) Cells sense and store locally available nutrients, transfer nutrients to neighboring cells, and duplicate when growth requirements are met. (c) These local actions produce coordinated growth of the root and shoot over time, with growth being measured by the plant size N and balance B .